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Cinematographic Camera Diffusion Model

Hongda Jiang, Xi Wang, Marc Christie, Libin Liu, Baoquan Chen

arXiv:2402.16143v1cs.GR

TL;DR

Natural-language camera directions can leave cinematographic properties implicit and admit multiple plausible trajectories, while text alone may not provide the control users need. The paper proposes a text-conditioned camera diffusion model with keyframe constraints and latent interpolation between motions. It reports advantages over recent techniques and finds keyframe conditioning superior to masking-based inpainting for balancing control and style.

  • Problem

    Textual camera directions can be ambiguous and leave implicit cinematographic properties for users to translate into specific camera motions.

    Source: §1 · p. 2

  • Method

    The method conditions a camera diffusion model on text-driven CLIP embeddings and keyframes, and uses embedding interpolation to blend generated motions.

    Source: §1 · p. 2

  • Results

    The authors report benefits over recent techniques and that keyframe conditioning is superior to masking-based inpainting for addressing the conflict between controllability and styled generation.

    Source: §1 · p. 3, §6.2 · p. 11

  • Takeaways & Limitations

    The approach provides camera-motion generation with keyframe control and transitions, including for partial textual descriptions.

    Source: §6.2 · p. 11

    The character-centric coordinate system can produce shaky motions with active characters, restrict complex multi-character compositions, and make character-absent scenes difficult to capture.

    Source: §6.1 · p. 10

Abstract

from arXiv · show

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization-based solving, encoding of empirical rules, learning from real examples,...), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer-based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high-level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text-to-camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at \URL{https://github.com/jianghd1996/Camera-control}.

1. Introduction

Turning cinematographic language into camera trajectories requires interpreting implicit film grammar and resolving ambiguity, while giving users precise control. The paper proposes a text-conditioned diffusion model with keyframe constraints and motion transitions.

1. Introduction

  • Natural-language camera directions leave implicit shot properties and multiple plausible interpretations, while users may lack the expertise to translate them into camera placements and motions.A tracking shot, for example, could use a back, side, or front view.Source: §1 · p. 2
  • The work introduces Camera Diffusion Model (CDM), using text-driven CLIP embeddings and diffusion stochasticity to generate diverse camera motions.It adds keyframe conditions and uses CLIP-embedding interpolation to blend generated sequences.Source: §1 · p. 2
  • The proposed system aims to provide linguistic camera-motion generation with keyframing and realistic transitions, including for linguistically ambiguous prompts.The authors describe it as a practical tool for artists and non-experts seeking precise control and cinematically pleasing results.Source: §1 · pp. 2–3
  • The authors report that keyframe conditioning addresses the conflict between controllability and styled generation, outperforming masking-based inpainting for this purpose.The paper also identifies keyframing and transitioning schemes as controls for that conflict.Source: §1 · p. 3
  • The paper reports quantitative and qualitative experiments, ablations, and showcases covering linguistic certainty, keyframing versus style, and zero-shot multimodality inference.Code and dataset are available from the project repository.Source: §1 · p. 3

2. Related Work

Related work spans computational cinematography, camera-control systems, generative models, and text-driven generation. The paper positions its approach as adapting motion diffusion to cinematography’s language ambiguity and control requirements.

2. Related Work

  • Computational cinematography includes narrative, mise-en-scène, lighting, camera control, and editing, with deep learning applied to several of these problems.Prior work also addresses constrained settings such as drone cinematography.Source: §2 · p. 3
  • Prior camera-control systems include drone methods with limited motion types and fine-grained control, plus virtual-camera generators based on examples and keyframes.The cited example-driven systems are described as ineffective for textual conditions.Source: §2 · p. 3
  • Diffusion models generate content by reversing a noise process, and guidance methods have been used to improve controllability and generation quality.The passage situates diffusion among generative approaches including autoencoders, variational autoencoders, and GANs.Source: §2 · p. 3
  • Text-driven generative systems combine linguistic input with generative models, including image, video, human-motion, and gesture applications.CLIP provides a joint image-text embedding space used in this line of work.Source: §2 · p. 3
  • This work builds on human-motion generation [TRG*23] and develops dedicated handling for cinematographic one-to-many and many-to-one language requirements, keyframes, and motion interpolation.The authors distinguish these additions from existing work.Source: §2 · p. 3

3. Overview

The system conditions camera-motion generation on text and optional endpoint keyframes, then denoises a latent sequence into a trajectory. It also supports transitions between sequences.

3. Overview

  • The system accepts partial or complete textual descriptions of framing, angle, and camera motion, with optional user-defined keyframes at trajectory endpoints.Text is encoded with CLIP and concatenated with keyframe information.Source: §3 · p. 3
  • At each diffusion step, latent noise Xt and the conditions are used to generate output noise Xt−1, with successive denoising steps producing the camera-motion sequence.Source: §3 · p. 3
  • The output sequence is designed to satisfy both the textual specification and keyframe constraints, while the system supports smooth transitions between sequences.Different strategies are provided for generating long sequences.Source: §3 · p. 3

4. Camera Diffusion Model

The CDM represents character-relative camera trajectories and learns conditioned denoising, with keyframe constraints and prompt variation providing additional control.

4.1. Data format

  • Each frame represents the camera’s position relative to the character and the normalized screen position of its look-at target; camera roll is omitted.Source: §4.1 · p. 4

4.2. Network design

  • Diffusion learns camera motion by adding noise to sequences and training a reverse denoising process, while text-only control and smooth segment transitions remain challenges.The method uses the stochastic process to generate multiple plausible outputs for an ambiguous many-to-many mapping.Source: §4.2 · p. 4

4.3. Keyframes as constraints in the diffusion process

  • Keyframes are added directly as diffusion conditions rather than imposed through inpainting, which the authors argue fails to resolve conflicts between style and constraints.The conditions combine CLIP-embedded shooting scripts with encoded starting and ending keyframes; training uses both keyframed and unkeyframed examples.Source: §4.3 · pp. 4–5

4.4.1. Synthetic dataset

  • The synthetic dataset combines camera-property variations into primitive trajectories and contains 25,000 sequences totaling 4,500,000 frames.It varies shot angle, scale, movement, velocity, and screen properties; time scaling adds different camera velocities.Source: §4.4.1 · p. 5

4.4.2. Prompt augmentation

  • Property-specific CLIP prompt templates and synonyms support varied descriptions, while randomly dropping prompts during training teaches generation from partial descriptions.Adding more detailed prompts can refine specifications and reduce variation across generations.Source: §4.4.2 · p. 5

4.4.3. Training details

  • Training uses a DDPM with 1,000 noising steps and supports maximum sequence lengths of 60 or 300 frames.The 60- and 300-frame models take about 60 and 90 hours, respectively, to converge; synthesizing 300 frames takes around 6 seconds.Source: §4.4.3 · pp. 5–6

4.5. Inference

  • At inference, the model denoises Gaussian noise under the conditions, with guidance magnitude ω controlling the balance between diversity and fidelity.The pipeline iteratively predicts noise and updates the sample toward a camera-motion sequence.Source: Figure 2 · p. 4, §4.5 · p. 6

5.1. Script-to-camera

The script-to-camera experiments compare text-conditioned methods and find that the proposed method outperforms LSTM+CLIP on almost all metrics. Prompt augmentation improves robustness and the variability of generated camera motions.

5.1. Script-to-camera

  • The proposed method outperforms LSTM+CLIP on almost all metrics in the script-to-camera evaluation.The reference LSTM+Gating method performs optimally but uses the full camera sequence rather than text embeddings.Source: §5.1 · p. 6, Table 1 · p. 6
  • Prompt augmentation improves textual-input robustness and increases the diversity and multimodality of generated camera motions.LSTM-based methods do not inherently support one-to-many generation, so their MultiModality entries are empty.Source: §5.1 · pp. 6–7
  • The authors choose CLIP for its cinematic-prompt embedding capability, while T5 embeddings also outperform the LSTM-based method.For T5, they freeze the text encoder and train an LSTM to aggregate its output tokens.Source: §5.1 · p. 7
  • Figure 3 compares inpainting, LSTM+CLIP, and the proposed method using the same rotation prompt and two keyframe constraints.Source: Figure 3 · p. 7

5.2. Keyframe-conditioned generation

The keyframe-conditioned experiments compare conditioning keyframes during denoising with inpainting and other methods. The authors report that conditioning outperforms inpainting, while far-to-style keyframes can conflict with the desired style.

5.2. Keyframe-conditioned generation

  • The condition method uses text and keyframes as denoising conditions, whereas inpainting fixes selected parts during denoising with a text-only condition.Source: §5.2 · p. 7
  • Keyframe distance averages L2 distances over the nearest three frames at the starting and ending keyframes to assess constraint satisfaction and continuity.Source: §5.2 · p. 7
  • The condition method outperforms inpainting in generating believable content while satisfying keyframe requirements and desired style.Source: §5.2 · p. 7, Table 2 · p. 7
  • With far-to-style keyframes, the required style is not well maintained when keyframes may conflict with the desired style.The authors report hundreds of distinct camera-movement pairs sharing identical keyframes; LSTM sequences are biased, whereas their method provides a correct interpolation scheme.Source: §5.2 · p. 7, Table 3 · p. 8
  • Figure 4 shows generated motion progressively converging to the overall input condition as descriptive information is appended to the script.Source: Figure 4 · p. 8

5.3. Qualitative studies and ablation

The studies examine how prompt specificity, image conditioning, embedding interpolation, keyframes, and velocity prompts shape camera-motion generation. They also report expert views on the tool’s potential and limits.

5.3.1. Variation vs. linguistic uncertainty of script

  • Vague prompts yield varied motions through denoising stochasticity, while added description narrows uncertainty and supports finer control.The study frames this as a many-to-many mapping: one script can correspond to multiple camera motions.Source: §5.3.1 · p. 8

5.3.2. Example-based generation

  • Real-film frame embeddings can guide zero-shot motions that capture framing, screen position, and camera motion, though their temporal imprecision can reduce precision.The method processes film frames with CLIP image embeddings and average pooling before conditioning generation.Source: §5.3.2 · p. 8, Figure 5 · p. 8

5.3.3. Zero-shot natural language generation

  • Prompt augmentation and CLIP’s zero-shot capacity handle non-strict cinematographic language, but erroneous outputs remain possible because CLIP lacks a cinematic-language bias.Examples include “circling” and “move further away.”Source: §5.3.3 · p. 8

5.3.4. Interpolation on CLIP space

  • Linear CLIP text-embedding interpolation moves trajectories smoothly from orbit to pan while preserving fixed keyframes.The example interpolates in 20% steps between the two styles.Source: §5.3.4 · p. 8

5.3.5. Transition schemes for long sequence generation

  • Different scripts, keyframes, and motion durations support believable, styled, controllable long sequences; automatic transitions avoid jump cuts between text-driven segments.With keyframes, only a few prompts are needed; text-only generation requires longer prompts specifying cinematic properties.Source: §5.3.5 · pp. 8–9

5.3.6. Velocity control

  • Velocity prompts vary how quickly a shot moves from long to close framing; faster motions use padded static frames that can be truncated at inference.The dataset includes motions at different camera velocities, with speed annotations based on trajectory speed.Source: §5.3.6 · pp. 9–10, Figure 9 · p. 10

5.3.7. Influence of ω

  • The authors select ω = 1 as the accuracy-fidelity sweet spot in their guidance-scale ablation.The ablation evaluates different classifier-free guidance scales.Source: §5.3.7 · p. 10, Table 4 · p. 10

5.3.8. Experts evaluation

  • Participants saw potential for non-professional use and routine-shot previz, appreciated automation, and raised concerns about creative limits and filmmaking’s broader decision needs.They also described efficiency comparable to a mediocre-pro cameraman and noted occasional overly fast orbit motions around rotating characters.Source: §5.3.8 · p. 10

6. Limitations and Conclusion

The paper identifies limits in semantic input and the character-centric representation, then concludes that diffusion can generate realistic camera motions relative to targets. It highlights data-related constraints on timing, sequential movement, and textual modifiers.

6.1. Limitation

  • Current methods lack rich language inputs that capture context and emotional correspondence aligned with real-film shooting data.The authors suggest deeper semantic input could improve generated-scene realism and applicability.Source: §6.1 · p. 10
  • The character-centric coordinate system can produce shaky motion with active characters and restrict complex multi-character or character-absent compositions.The authors cite long shots with complex trajectories and establishing or touring shots as affected cases.Source: §6.1 · p. 10

6.2. Conclusion

  • The conclusion identifies precise timing, sequential movements, and richer modifiers such as faster or smoother as limitations, mostly because available data are limited.It points to more precise descriptions from real film clips and a richer dataset as ongoing challenges.Source: §6.2 · p. 11

Camera representation and prompt templates

Camera motions are represented relative to a character using position and screen-target coordinates, with spherical variables and prompt templates expressing cinematic properties.

Camera representation and prompt templates

  • Each trajectory is a sequence of 5DoF parameters: camera position and normalized screen position of the look-at target.The representation also uses local spherical coordinates for elevation, azimuth, and distance.Source: Camera representation and prompt… · p. 13
  • Angle prompts encode high, eye-level, or low views, while view-direction prompts specify eight orientations around the character.The direction angles include a ±0.05π variation.Source: Camera representation and prompt… · p. 14
  • Shot-scale prompts map distance relative to character height to categories from extreme close-up through extreme long shot.The listed distance ranges use character height h as their reference.Source: Camera representation and prompt… · p. 14
  • Screen-position prompts divide normalized coordinates into left, center, or right and top, middle, or bottom regions.The screen coordinate system spans [−1,1] × [−1,1].Source: Camera representation and prompt… · p. 14
  • The motion templates define static, push/pull, pan, boom, and orbit through changes in camera position, distance, or orientation relative to the target.Orbit swivels horizontally around the target; boom translates the camera vertically.Source: Camera representation and prompt… · p. 14
  • Velocity prompts label fast, normal, and slow motions using frame-duration ranges at 30 fps.The stated ranges are 75–105, 160–200, and 240–300 frames, respectively.Source: Camera representation and prompt… · p. 14

Appendix B: Network structure

The appendix describes a classification network’s layer notation and training setup.

Appendix B: Network structure

  • The classifier architecture uses fully connected layers, transformer encoder layers, ReLU activations, and data length L.The architecture table reports input and output channel counts in its rightmost column.Source: Appendix B · p. 14
  • The classification network is optimized with cross-entropy loss implemented in PyTorch.Source: Appendix B · p. 14
  • The architecture table summarizes the classifier’s layers and channel counts.Source: Appendix B · p. 14
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